Adaptive Multi-Agent Systems (AMAS) transform dynamic problems into problems
of local cooperation between agents. We present smapy, an ensemble based AMAS
implementation for mobility prediction, whose agents are provided with machine
learning models in addition to their cooperation rules. With a detailed
methodology, we propose a framework to transform a classification problem into
a cooperative tiling of the input variable space. We show that it is possible
to use linear classifiers for online non-linear classification on three
benchmark toy problems chosen for their different levels of linear
separability, if they are integrated in a cooperative Multi-Agent structure.
The results obtained show a significant improvement of the performance of
linear classifiers in non-linear contexts in terms of classification accuracy
and decision boundaries, thanks to the cooperative approach.

该论文介绍了 smapy，一个基于集成的 AMAS 实现，应用于移动预测，其代理具备机器学习模型以及合作规则，通过协作式的多智能体结构中将分类问题转化为输入变量空间的合作瓦片图。实验结果表明，线性分类器在非线性场景下经过合作式的多智能体方法，在分类准确性和决策边界方面显示出了显着的改进。